Method and system for minimum mean squared error soft interference cancellation (MMSE-SIC) Based suboptimal maximum likelihood (ML) detection for multiple input multiple output (MIMO) wireless system

ABSTRACT

Various aspects of a method for minimum mean square error soft interference cancellation (MMSE-SIC) based sub-optimal maximum likelihood (ML) detection for a multiple input multiple output (MIMO) wireless system may comprise selecting at least one constellation point in a constellation map based on at least one of a plurality of received symbols. A number of the at least one constellation point may be less than or equal to a number of previously selected constellation points in a previous constellation map. At least one of the plurality of received symbols may be decoded based on the selected at least one constellation point.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

Not Applicable.

FIELD OF THE INVENTION

Certain embodiments of the invention relate to wireless communications.More specifically, certain embodiments of the invention relate to amethod and system for minimum mean squared error soft interferencecancellation (MMSE-SIC) based suboptimal maximum likelihood (ML)detection for multiple input multiple output (MIMO) wireless systems.

BACKGROUND OF THE INVENTION

An RF communications system may comprise a transmitter and a receiverthat communicate via a radio frequency (RF) channel. The transmitter mayencode information in a symbol that is transmitted via the RF channel.The transmitter may utilize a modulation type to encode information intoa symbol, s. The modulation type may comprise a plurality ofconstellation points that represent distinct combinations of binarybits. The transmitter may encode information comprising binary bits ofinformation by selecting a modulation type, and within the selectedmodulation type, selecting a constellation point to represent the binarybits of information. A binary bit may also be referred to as a bit. Thetransmitter may generate signals corresponding to the constellationpoint that may comprise in-phase (I) and quadrature phase (Q) signals.The correlation between a constellation point and I and Q signals maycomprise a mapping. The I and Q signals may be transmitted by thetransmitter as an IQ signal via the RF channel.

The RF channel may distort the transmitted IQ signal from thetransmitter such that, at the receiver, the received signals I_(R) andQ_(R) may differ in magnitude and/or phase from the correspondingtransmitted signals I and Q. In addition, the RF channel may introducenoise into the signal.

A task for a receiver in achieving successful reception of information,via the RF channel, from the transmitter may comprise a plurality ofsteps to determine, based on a received I_(R)Q_(R) signal, the binarybits, of information that were transmitted by the transmitter. One stepmay comprise detecting a symbol from the received I_(R)Q_(R) signal. Thereceiver may utilize a modulation type to decode the I_(R)Q_(R) signal.The receiver may utilize a corresponding modulation type to themodulation type utilized by the transmitter. The correlation between thesignals I_(R) and Q_(R) and a constellation point may comprise ademapping. Because the signals I_(R) and Q_(R) at the receiver maydiffer from the corresponding signals I and Q at the transmitter, thereceiver may be unable to correlate the signals I_(R) and Q_(R) to aconstellation point. The receiver may utilize various heuristics todemap the signals I_(R) and Q_(R) to a constellation point. The selectedconstellation point may comprise an estimate, ŝ, of the symbol, s, thatwas transmitted by the transmitter.

Since the receiver utilizes an estimate of the transmitted signal, ŝ,there is a statistical probability that the constellation pointassociated with estimate ŝ may differ from the correspondingconstellation point associated with original symbol, s. Consequently,there is a statistical probability that at least one binary bit ofinformation retrieved from the estimate, ŝ, may differ from acorresponding binary bit of information in the original symbol, s, thatwas transmitted by the transmitter. Such a difference in one or morereceived binary bits of information may constitute a communicationserror between the transmitter and the receiver that may be measured by apacket error rate (PER) statistic.

The task of assessing the statistical probability that at least onebinary bit of information in an estimate, ŝ, is equivalent to acorresponding binary bit of information in an original symbol, s, maycomprise comparing the estimate ŝ to a plurality of constellation pointsin a constellation map. In some conventional approaches, the task ofevaluating these comparisons may be of exponential complexity as thenumber of constellation points in a constellation map may increaseexponentially with an increase in the number of binary bits contained inan original symbol, s. A comparison between the estimate ŝ and at leastone constellation point in a constellation map may be referred to as a“search”. A comparison between the estimate ŝ and each of the pluralityof constellation points in a constellation map may be referred to as a“full search”. The number of comparisons in a full search may increaseexponentially with an increase in the number of bits contained in theoriginal symbol, s.

A transmitter may also utilize a plurality of symbols to encode aplurality of binary bits of information. The transmitter may transmitthe plurality of symbols concurrently. Each of the plurality of symbolsmay be transmitted by a corresponding plurality of transmittingantennas. Each of the plurality of symbols may be transmitted in acorresponding stream. The stream transmitted by each transmittingantenna may be referred to as a “layer”. At the receiver, the receivedplurality of symbols may be compared to constellation points in aconstellation map in which the number of comparisons may also increaseexponentially with an increase in the number of symbols in thetransmitted plurality of symbols. Alternatively, each symbol in thereceived plurality of symbols may be associated with a correspondingconstellation map for the symbol. Within a layer, a symbol in a receivedplurality of symbols may be compared to constellation points in aconstellation map. Jointly comparing each symbol in the receivedplurality of symbols to constellation points in each correspondingconstellation map may still result in an exponential increase in thenumber of comparisons with an increase in the number of symbols in thetransmitted plurality of symbols.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of such systems with some aspects of the present invention asset forth in the remainder of the present application with reference tothe drawings.

BRIEF SUMMARY OF THE INVENTION

A system and/or method is provided for minimum mean squared error softinterference cancellation (MMSE-SIC) based suboptimal maximum likelihood(ML) detection for multiple input multiple output (MIMO) wirelesssystems, substantially as shown in and/or described in connection withat least one of the figures, as set forth more completely in the claims.

These and other advantages, aspects and novel features of the presentinvention, as well as details of an illustrated embodiment thereof, willbe more fully understood from the following description and drawings.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an exemplary multiple inputmultiple output (MIMO) communications system that may be utilized inconnection with an embodiment of the invention.

FIG. 2 is a block diagram of an exemplary MIMO transceiver system inaccordance with an embodiment of the invention.

FIG. 3 is a graph illustrating exemplary results from simulation of asystem for minimum mean squared error with soft interferencecancellation (MMSE-SIC) based on suboptimal maximum likelihood (ML)detection for a MIMO-OFDM wireless system, in accordance with anembodiment of the invention.

FIG. 4A is a diagram illustrating an exemplary method for softinformation calculation, in accordance with an embodiment of theinvention.

FIG. 4B is a diagram illustrating an exemplary soft informationcalculation, in accordance with an embodiment of the invention.

FIG. 5 is a flow chart illustrating exemplary steps for softinterference cancellation (SIC), in accordance with an embodiment of theinvention.

FIG. 6 is a flow chart illustrating exemplary steps in the selection ofconstellation points to be utilized in MMSE-SIC for a spatial streamcomprising a symbol received by an antenna in accordance with anembodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

Certain embodiments of the invention may be found in a method and systemfor minimum mean squared error soft interference cancellation (MMSE-SIC)based suboptimal maximum likelihood (ML) detection for multiple inputmultiple output (MIMO) wireless systems. Various embodiments of theinvention may utilize statistical analytical methods to reducecommunications errors between a transmitter and a receiver communicatingvia an RF channel by improving the statistical likelihood that areceived binary bit of information does not differ from thecorresponding binary bit of information that was transmitted. Variousaspects of a system for MMSE soft interference cancellation (MMSE-SIC)based on suboptimal ML detection for a multiple input multiple output(MIMO) wireless system may comprise utilizing log likelihood ratios(LLR) in a reduced complexity ML detector.

Various embodiments of the invention may achieve reduced complexity incomparison to alternative approaches to the task of ML detection. Oneaspect of the invention may reduce complexity by reducing the number ofcomparisons between a received plurality of symbols and constellationpoints in a constellation map. In relation to a full search method,various embodiments of the invention may compare a estimate ŝ, in alayer, to a number of constellation points in a correspondingconstellation map where the number of comparisons may be less than thenumber of constellation points in the corresponding constellation map.

FIG. 1 is a block diagram illustrating an exemplary multiple inputmultiple output (MIMO) communications system that may be utilized inconnection with an embodiment of the invention. Referring to FIG. 1there is shown a transmitting mobile terminal 104, a plurality oftransmitting antenna 112, 114, and 116, a receiving mobile terminal 124,a plurality of receiving antenna 132, 134, and 136, a plurality of RFchannels 142, and a noise source 140.

In operation, the transmitter 104 may transmit signals via the pluralityof transmitting antennas 112, 114, and 116. A signal may comprise atransmitted symbol. An independent signal transmitted by the pluralityof transmitting antennas 112, 114, and 116 may represent a spatialstream. A signal transmitted by the transmitter 104 may be consideredindependent if it comprises information that is independent compared toanother signal transmitted by the transmitter 104, during anapproximately simultaneous period of time. For a given number, NTX, oftransmitting antennas 112, 114, and 116, the number of spatial streams,NSS, transmitted by the transmitter 104 may be greater than or equal to1, and less than or equal to NTX. If NSS is equal to NTX, then each ofthe plurality of transmitting antennas 112, 114, and 116 may transmitindependent signals during an approximately simultaneous period of time.

The transmitter 104 may transmit a plurality of symbols s₁, s₂, and s₃,via corresponding transmitting antenna 112, 114, and 116 respectively.The symbols may be transmitted via an RF channel 142 where they may besubjected to scaling by a transfer function matrix, H, that isassociated with transmission via the RF channel 142. A transfer functionmatrix of the RF channel 142, H, may be derived at the receiver 124based on a channel estimate. Furthermore, noise from the noise source140 may be added to the transmitted symbols. During communication withthe transmitter 104, the receiver 124 may receive signals y₁, y₂, andy₃, via receiving antennas 132, 134, and 136 respectively, where atleast one received signal comprises at least a portion of the symbolss₁, s₂, and s₃ that were transmitted by the transmitter 104. Forexample, given independent signals from the transmitting antennas 112,114, and 116, the signal y₁ may be expressed:y ₁ =h ₁₁ x ₁ +h ₁₂ x ₂ ×h ₁₃ x ₃ +n ₁  equation[1]where x_(i) may represent an estimate of the corresponding symbol s_(i),n₁ may represent noise introduced into the RF channel 142 by a noisesource 140, and h_(ni) may represent a coefficient from the transferfunction matrix H that is applied to a signal transmitted bytransmitting antenna i, and received by receiving antenna n.

Equation[1] may be generalized to express a relationship between aplurality of signals, S, transmitted by a transmitter 104, and a signal,Y, comprising a plurality of signals received by a receiver 124:Y=HX+N  equation[2]where Y={y₁, y₂, . . . y_(NRX)} may be represented as an NRX×1 (NRXrows, 1 column) matrix with NRX representing a number of receivingantenna, X={x₁, x₂, . . . x_(NSS)} may be represented as an NSS×1matrix, H may be represented as a NRX×NTX matrix {{h₁₁, h₁₂, . . .h_(1,NTX)}{h₂₁, h₂₂, . . . h_(2,NTX)}{h_(NRX,1), h₃₂, h_(NRX,NTX)}}; andthe noise vector N may be represented as is a NRX×1 matrix {n₁, n₂, . .. n_(NRX)}.

An estimate for the vector X from equation[2] may be computed based onthe received signal vector Y as shown in the following equation:X=WY  equation[3]where W may represent a matrix.

The matrix W from equation[3] may be computed by a linear equalizationmethod. When utilizing zero forcing linear equalization (ZF-LE), thematrix W may be computed based on the transfer function matrix H asshown in the following equation: $\begin{matrix}{W = {\frac{1}{H^{*}H}H^{*}}} & {{equation}\quad\lbrack 4\rbrack}\end{matrix}$where H* may represent a complex conjugate transpose of the matrix H.

When utilizing minimum mean squared error linear equalization (MMSE-LE),the matrix W may be computed based on the transfer function matrix H,and a signal to noise ratio (SNR) based on the received signal vector Yand noise vector N, as shown in the following equation: $\begin{matrix}{W = {\frac{1}{\left( {{H^{*}H} + {\frac{1}{SNR}I}} \right)}H^{*}}} & {{equation}\quad\lbrack 5\rbrack}\end{matrix}$where I may represent an identity matrix.

In the exemplary MIMO communications system illustrated in FIG. 1, theplurality of received signals received by receiver 124 may compriseY={y₁, y₂, y₃}, the plurality of signals transmitted by transmitter 104may comprise S={s₁, s₂, s₃}, the transfer function matrix of the RFchannel 142 may comprise H={{h₁₁, h₁₂, h₁₃}{h₂₁, h₂₂, h₂₃}{h₃₁, h₃₂,h₃₃}}; and the noise source 140 may comprise N={n₁, n₂, n₃}.

A receiver 124 may utilize maximum likelihood (ML) detection todetermine a likelihood that the binary value of a received bit, x,decoded from an estimated symbol, ŝ, that was derived from a receivedsignal, Y, is equal to the binary value of a corresponding bit in asymbol, s, that was transmitted by a transmitter 104. For example, basedon a log likelihood ratio (LLR) analysis, a likelihood that the value ofan i^(th) bit, in a symbol transmitted by an n^(th) transmitting antenna112, given received signal Y at the receiver 124, is equal to binary 1,or an L-value of a bit x_(ni), L(X_(ni)|Y), may be expressed as:$\begin{matrix}{{L\left( x_{ni} \middle| Y \right)} = {\ln\left( \frac{P\left( {x_{ni} = \left. 1 \middle| Y \right.} \right)}{P\left( {x_{ni} = \left. 0 \middle| Y \right.} \right)} \right)}} & {{equation}\quad\lbrack 6\rbrack}\end{matrix}$where x_(ni) may represent an i^(th) bit, in a symbol transmitted by ann^(th) transmitting antenna 112. The probabilities P(x_(ni)=1|Y), andP(x_(ni)=0|Y), may represent the a priori probabilities that x_(ni)equals 1 and 0 respectively, given a received signal Y.

Applying Bayes' rule, equation[6] may also be expressed as:$\begin{matrix}{{L\left( x_{ni} \middle| Y \right)} = {\ln\left( \frac{{P\left( {\left. Y \middle| x_{ni} \right. = 1} \right)}{{P\left( {x_{ni} = 1} \right)}/{p(Y)}}}{{P\left( {\left. Y \middle| x_{ni} \right. = 0} \right)}{{P\left( {x_{ni} = 0} \right)}/{p(Y)}}} \right)}} & {{equation}\quad\lbrack 7\rbrack}\end{matrix}$where the probabilities p(Y|x_(ni)=1), and p(Y|x_(ni)=0), may representthe probabilities that a received signal Y was accurately received bythe receiver 124 given that bit x_(ni) equals 1 and 0 respectively. Theprobabilities P(x_(ni)=1), and P(x_(ni)=0), may represent the a prioriprobabilities that bit x_(ni) equals 1 and 0 respectively. Theprobability p(Y) may represent the probability of receiving signal Y.

Accordingly, equation[7] may further be expressed: $\begin{matrix}{{L\left( x_{ni} \middle| Y \right)} = {{\ln\left( \frac{P\left( {x_{ni} = 1} \right)}{P\left( {x_{ni} = 0} \right)} \right)} + {\ln\left( \frac{P\left( {\left. Y \middle| x_{ni} \right. = 1} \right)}{P\left( {\left. Y \middle| x_{ni} \right. = 0} \right)} \right)}}} & {{equation}\quad\lbrack 8\rbrack}\end{matrix}$where the first term on the right hand side of equation[8] may representan a priori L-value of a bit x_(ni), or L_(A,ni), and the second term onthe right hand side of equation[8] may represent an expectation valuefor the probability p(Y|x) over a range of values of x_(ni) which may beselected from among a range of values comprising {x_(ni)=1, x_(ni)=0}.

The second term on the right hand side of equation[8] may be evaluatedby considering a range of possible combinations of binary bits in areceived signal Y. For a received signal Y comprising a plurality of Dbinary bits, there may 2^(D) possible combinations of binary bits in thesignal Y. The plurality of D binary bits may be represented as X. Theprobability p(Y|x_(ni)=1) in equation[8] may represent a number ofpossible combinations of binary bits in the received signal Y for whichx_(ni)=1. The probability p(Y|x_(ni)=0) in equation[8] may represent anumber of possible combinations of binary bits in the received signal Yfor which x_(ni)=0. Given this, equation[8] may be expressed:$\begin{matrix}{{L\left( x_{ni} \middle| Y \right)} = {L_{A,{ni}} + {\ln\left( \frac{\sum\limits_{x_{ni} = 1}{{p\left( Y \middle| X \right)}{P\left( X \middle| x_{ni} \right)}}}{\sum\limits_{x_{ni} = 0}{{p\left( Y \middle| X \right)}{P\left( X \middle| x_{ni} \right)}}} \right)}}} & {{equation}\quad\lbrack 9\rbrack}\end{matrix}$

Lacking any a priori basis to expect that the probability that x_(ni)=1is not equal to the probability that x_(ni)=0, the first term ofequation[9] may be equal to 0. Furthermore, a Max-Log approximation maybe applied to simplify equation[9]: $\begin{matrix}{{L\left( x_{ni} \middle| Y \right)} \approx {{\max\limits_{x_{ni} = 1}\left\{ {{\ln\quad{p\left( Y \middle| X \right)}} + {\ln\quad{P\left( X \middle| x_{ni} \right)}}} \right\}} - {\max\limits_{x_{ni} = 0}\left\{ {{\ln\quad{p\left( Y \middle| X \right)}} + {\ln\quad{P\left( X \middle| x_{ni} \right)}}} \right\}}}} & {{equation}\quad\lbrack 10\rbrack}\end{matrix}$where the first term on the right hand side of equation[10] mayrepresent a statistically most probable, or most likely, combination ofbinary bits in the received signal Y for which a bit x_(ni) in thatcombination is equal to a binary value of 1. The natural logarithm ofthe probability of a most likely combination may be represented asΛ(X,Y). The second term on the right hand side of equation[10] mayrepresent a statistically most probable, or most likely, combination ofbinary bits in the received signal Y for which a bit x_(ni) in thatcombination is equal to a binary value of 0.

Based on equation[10], an a posteriori L-value may be expressed:$\begin{matrix}{{L\left( x_{ni} \middle| Y \right)} \approx {{\max\limits_{x_{ni} = 1}{\Lambda\left( {X,Y} \right)}} - {\max\limits_{x_{ni} = 0}{\Lambda\left( {X,Y} \right)}}}} & {{equation}\quad\lbrack 11\rbrack}\end{matrix}$

Based on an additive Gaussian white noise (AWGN) distribution forprobabilities associated with the received signal Y in equation[2]:$\begin{matrix}{{p\left( Y \middle| X \right)} = {\frac{1}{\sqrt{2\quad\pi}N_{0}}{\mathbb{e}}^{\frac{{{Y - {{Hs}{(x)}}}}^{2}}{2\quad N_{0}}}}} & {{equation}\quad\lbrack 12\rbrack}\end{matrix}$where N₀ may represent a variance of noise introduced by a noise source140, s(x) may represent a constellation point, and x may representbinary bits that may be demapped from the constellation point s(x). Theexpression ∥A∥ may represent a scalar magnitude of a vector A.

Thus, Λ(X,Y) may be expressed as: $\begin{matrix}{{\Lambda\left( {X,Y} \right)} = {{- \frac{1}{N_{0}}}{{Y - {{Hs}(x)}}}^{2}}} & {{equation}\quad\lbrack 13\rbrack}\end{matrix}$

Substituting the expression for Λ(X,Y) from equation[13] intoequation[11] may produce: $\begin{matrix}{{L\left( {x_{ni}\text{❘}Y} \right)} = {{\max\limits_{x_{ni} = 1}\left\{ {- {{Y - {{Hs}(x)}}}^{2}} \right\}} - {\max\limits_{x_{ni} = 0}\left\{ {- {{Y - {{Hs}(x)}}}^{2}} \right\}}}} & {{equation}\quad\lbrack 14\rbrack}\end{matrix}$that may be further expressed: $\begin{matrix}{{L\left( {x_{ni}\text{❘}Y} \right)} = {{\min\limits_{x_{ni} = 0}\left\{ {{Y - {{Hs}(x)}}}^{2} \right\}} - {\min\limits_{x_{ni} = 1}\left\{ {{Y - {{Hs}(x)}}}^{2} \right\}}}} & {{equation}\quad\lbrack 15\rbrack}\end{matrix}$

The first term on the right hand side of equation[15] may represent aminimum mean squared error for a most likely combination of bits Xcomprising x_(ni)=0. The second term on the right hand side ofequation[15] may represent a magnitude minimum mean squared error (MMSE)for a most likely combination of bits X comprising x_(ni)=1. As such, anL-value based on equation[15] expresses a difference between magnitudeMMSEs. A magnitude MMSE may also be referred to as a magnitude, or as anMMSE. If the magnitude MMSE for a desired outcome, for example x_(ni)=1,is significantly less than the magnitude MMSE for an undesired outcome,for example x_(ni)=0, the L-value may be large. A large L-value mayreflect a higher degree of statistical confidence, which indicates thata detected bit x_(ni)=1 at the receiver 124 actually is a value for acorresponding bit transmitted by the transmitter 104. An L-valuegenerated by a detector may comprise soft information that may beutilized by a decoder to decode at least one binary bit derived from anestimated symbol ŝ.

In a signal Y associated with independent signals, or layers, x₁, x₂, .. . x_(NSS), ZF-LE may be based on cancelling interference associatedwith a layer x_(i), from the signal Y by projecting a desired signalx_(j) in a vector subspace that may be perpendicular to the signalx_(i). One potential limitation associated with this method is thatZF-LE may result in a loss of useful signal energy which may, in turn,result in noise enhancement. This may result in reduced SNR. MMSE-LE mayutilize a minimum mean squared error criteria but may result in anincreased packet error rate (PER).

By comparison, maximum likelihood (ML) equalization may represent a moreoptimal solution but may be comparatively very complex. An MLequalization implementation may involve a search through a plurality ofall candidate vectors contained in a multiple-dimension space. The sizeof the multiple-dimension space may be determined based on the number oflayers, and the modulation type utilized with each layer. Consequently,the complexity of the ML equalization implementation may increaseexponentially based on the number of layers and the number of points ina constellation associated with the corresponding modulation typeutilized with each layer.

FIG. 2 is a block diagram of an exemplary MIMO transceiver system inaccordance with an embodiment of the invention. Referring to FIG. 2there is shown a transceiver comprising a transmitter 200, a receiver201, a processor 240, a baseband processor 242, a plurality oftransmitting antennas 215 a . . . 215 n, and a plurality of receivingantennas 217 a . . . 217 n. The transmitter 200 may comprise a codingblock 202, a puncture block 204, an interleaver block 206, a pluralityof mapper blocks 208 a . . . 208 n, and a plurality of digital to analog(D to A) conversion and antenna front end blocks 214 a . . . 214 n. Thereceiver 201 may comprise a plurality of antenna front end and analog todigital (A to D) conversion blocks 216 a . . . 216 n, a detector block224, a plurality of demapper blocks 226 a . . . 226 n, a deinterleaverblock 228, a depuncture block 230, and a decoder block 232.

The processor 240 may perform upper layer protocol functions inaccordance with applicable communications standards. These functions maycomprise, but are not limited to, tasks performed at lower layers in arelevant protocol reference model. These tasks may further comprisephysical layer convergence procedure (PLCP), physical medium dependent(PMD) functions, and associated layer management functions. The basebandprocessor 242 may perform lower layer protocol functions in accordancewith applicable communications standards. These functions may comprise,but are not limited to, tasks related to analysis of data received bythe receiver 201, and tasks related to generating data to be transmittedby the transmitter 200. These tasks may further comprise medium accesscontrol (MAC) layer functions as specified by pertinent standards.

In the transmitter 200, the coding block 202 may transform receivedbinary input data blocks by applying a forward error correction (FEC)technique, for example, binary convolutional coding (BCC). Theapplication of FEC techniques, also known as “channel coding”, mayimprove the ability to successfully recover transmitted data at areceiver by appending redundant information to the input data prior totransmission via an RF channel. The ratio of the number of bits in abinary input data block to the number of bits in a transformed datablock may be known as the “coding rate”. The coding rate may bespecified using the notation i_(b)/t_(b), where t_(b) represents thetotal number of bits that may comprise a coding group of bits, whilei_(b) represents the number of information bits that may be contained inthe group of bits t_(b). Any number of bits t_(b)−i_(b) may representredundant bits that may enable the receiver 201 to detect and correcterrors introduced during transmission. Increasing the number ofredundant bits may enable greater capabilities at the receiver to detectand correct errors in information bits. The invention is not limited toBCC, and any one of a plurality of coding techniques, for example, Turbocoding or low density parity check (LDPC) coding, may also be utilized.

The puncture block 204 may receive transformed binary input data blocksfrom the coding block 202 and alter the coding rate by removingredundant bits from the received transformed binary input data blocks.For example, if the coding block 202 implemented a ½ coding rate, 4 bitsof data received from the coding block 202 may comprise 2 informationbits, and 2 redundant bits. By eliminating 1 of the redundant bits inthe group of 4 bits, the puncture block 204 may adapt the coding ratefrom ½ to ⅔. The interleaver block 206 may rearrange bits received in acoding rate-adapted data block from the puncture block 204 prior totransmission via an RF channel to reduce the probability ofuncorrectable corruption of data due to burst of errors, impactingcontiguous bits, during transmission via an RF channel. The output fromthe interleaver block 206 may also be divided into a plurality ofstreams where each stream may comprise a non-overlapping portion of thebits from the received coding rate-adapted data block. Therefore, for agiven number of bits in the coding rate-adapted data block, b_(db), agiven number of streams from the interleaver block 206, n_(st), and agiven number of bits assigned to an individual stream i by theinterleaver block 206, b_(st)(i): $\begin{matrix}{b_{db} = {\sum\limits_{i = 0}^{n_{st} - 1}{b_{st}(i)}}} & {{equation}\quad\lbrack 16\rbrack}\end{matrix}$

For a given number of coded bits before interleaving, b_(db), each bitmay be denoted by an index, k=0, 1 . . . b_(db)−1. The interleaver block206 may assign bits to the first spatial stream, spatial stream 0,b_(st)(0), for bit indexes k=0, n_(st), 2*n_(st), . . . , b_(db)−n_(st).The interleaver block 206 may assign bits to spatial stream 1,b_(st)(1), for bit indexes k=1, n_(st)+1, 2*n_(st)+1, . . . ,b_(db)−n_(st)+1. The interleaver block 206 may assign bits to spatialstream 2, b_(st)(2), for bit indexes k=2, n_(st)+2, 2*n_(st)+2, . . . ,b_(db)−n_(st)+2. The interleaver block 206 may assign bits to spatialstream n_(st), b_(st)(n_(st)), for bit indexes k=n_(st)−1, 2*n_(st)−1,3*n_(st)−1, . . . , b_(db)−1.

The plurality of mapper blocks 208 a . . . 208 n may comprise a numberof individual mapper blocks that is equal to the number of individualstreams generated by the interleaver block 206. Each individual mapperblock 208 a . . . 208 n may receive a plurality of bits from acorresponding individual stream, mapping those bits into a “symbol” byapplying a modulation technique based on a “constellation” utilized totransform the plurality of bits into a signal level representing thesymbol. The representation of the symbol may be a complex quantitycomprising in-phase (I) and quadrature (Q) components. The mapper block208 a . . . 208 n for stream i may utilize a modulation technique to mapa plurality of bits, b_(st)(i), into a symbol.

The plurality of digital (D) to analog (A) conversion and antenna frontend blocks 214 a . . . 214 n may receive the plurality of signalsgenerated by the plurality of mapper blocks 208 a . . . 208 n. Thedigital signal representation received from each of the plurality ofmapper blocks 208 a . . . 208 n may be converted to an analog RF signalthat may be amplified and transmitted via an antenna. The plurality of Dto A conversion and antenna front end blocks 214 a . . . 214 n may beequal to the number of transmitting antenna 215 a . . . 215 n. Each D toA conversion and antenna front end block 214 a . . . 214 n may utilizean antenna 215 a . . . 215 n to transmit one RF signal via an RFchannel.

In the receiver 201, the plurality of antenna front end and A to Dconversion blocks 216 a . . . 216 n may receive analog RF signals via anantenna, converting the RF signal to baseband and generating a digitalequivalent of the received analog baseband signal. The digitalrepresentation may be a complex quantity comprising I and Q components.The number of antenna front end and A to D conversion blocks 216 a . . .216 n may be equal to the number of receiving antenna 217 a . . . 217 n.

The channel estimates block 222 may utilize preamble information,contained in a received RF signal, to compute channel estimates. Thedetector block 224 may receive signals generated by the plurality ofantenna front end blocks 216 a . . . 216 n. The detector block 224 mayprocess the received signals based on input from the channel estimatesblock 222 to recover the symbol originally generated by the transmitter200 the detector block 224 may comprise suitable logic, circuitry,and/or code that may be adapted to transform symbols received from theplurality of antenna front end blocks 216 a . . . 216 n to compensatefor fading in the RF channel.

The plurality of demapper blocks 226 a . . . 226 n may receive symbolsfrom the detector block 224, reverse mapping each symbol to one or morebinary bits by applying a demodulation technique, based on themodulation technique utilized in generating the symbol at thetransmitter 200. The plurality of demapper blocks 226 a . . . 226 n maybe equal to the number of streams in the transmitter 200.

The deinterleaver block 228 may receive a plurality of bits from each ofthe demapper blocks 226 a . . . 226 n, rearranging the order of bitsamong the received plurality of bits. The deinterleaver block 228 mayrearrange the order of bits from the plurality of demapper blocks 226 a. . . 226 n in, for example, the reverse order of that utilized by theinterleaver 206 in the transmitter 200. The depuncture block 230 mayinsert “null” bits into the output data block received from thedeinterleaver block 228 that were removed by the puncture block 204. Thedecoder block 232 may decode a depunctured output data block, applying adecoding technique that may recover the binary data blocks that wereinput to the coding block 202.

In operation, the detector 224 may perform statistical analysis on areceived symbol. The received symbol may be estimated and mapped to aplurality of candidate constellation points in a constellation based ona modulation type. The plurality of candidate constellation points maycomprise a subset of the total number of constellation points in theconstellation. Based on the statistical analysis, the detector 224 maygenerate soft information. The decoder 232 may utilize the softinformation in decoding a plurality of binary bits received from thedepuncture block 230. The decoder may determine a binary value for atleast one of the plurality of binary bits received from the depunctureblock 230.

The processor 240 may receive decoded data from the decoder 232. Theprocessor 240 may communicate received data to the baseband processor242 for analysis and further processing. The processor 240 may alsocommunicate data received via the RF channel, by the receiver 201, tothe channel estimates block 222. This information may be utilized by thechannel estimates block 222, in the receiver 201, to compute channelestimates for a received RF channel. The baseband processor 242 maygenerate data to be transmitted via an RF channel by the transmitter200. The baseband processor 242 may communicate the data to theprocessor 240. The processor 240 may generate a plurality of bits thatare communicated to the coding block 202.

The elements shown in FIG. 2 may comprise components that may be presentin an exemplary embodiment of a wireless communications terminal. Oneexemplary embodiment may be a wireless communications transmittercomprising a transmitter 200, a processor 240, and a baseband processor242. Another exemplary embodiment may be a wireless communicationsreceiver comprising a receiver 201, a processor 240, and a basebandprocessor 242. Another exemplary embodiment may be a wirelesscommunications transceiver comprising a transmitter 200, a receiver 201,a processor 240, and a baseband processor 242.

In an ML implementation, an LLR may be computed as described inequation[15] for each of a plurality of candidate vectors. Each vectormay comprise a plurality of symbols. Each symbol may comprise aplurality of bits. The total number of candidate vectors, L, mayincrease exponentially with the number of layers, or spatial steams, andwith the number of constellation points associated with the modulationtype utilized with each corresponding spatial stream as described in thefollowing equation: $\begin{matrix}{L = {\prod\limits_{i = 1}^{NSS}{M\lbrack i\rbrack}}} & {{equation}\quad\lbrack 17\rbrack}\end{matrix}$where M[i] may represent the number of constellation points associatedwith the modulation type utilized with the i^(th) spatial stream.

Various embodiments of the invention may represent a reduced complexityML implementation that computes an LLR for each of a plurality ofcandidate vectors L′, where the number of candidate vectors L′ is lessthan the total number of candidate vectors L. The plurality of candidatevectors L′ may be selected based on a magnitude error value, ∥Y−HX∥,where Y may represent a received signal vector, H may represent atransfer function matrix, and X may represent a vector of estimates forindividual transmitted signals. Minimum mean squared error with softinterference cancellation (MMSE-SIC) may represent a method forselecting the set of candidate vectors L′, in accordance with anembodiment of the invention.

ZF-LE is an example of a method for performing cancellation of a layer ifrom the signal vector Y by cancelling an estimate for a symbol x_(i).The residual signal vector Y′ may be utilized, in conjunction with arecomputed channel estimate matrix H′, to compute a new estimate of thesymbol vector X′. This process may be repeated for each of thesubsequent layers among the plurality of NSS layers. If the estimatedvalue for a layer x_(i) is not equal to the value of the correspondingtransmitted symbol s_(i), the value x_(i) may considered to be detectedin error. If the erroneous value x_(i) is utilized for cancellation of alayer, it may result in a propagation of that error when utilizing thecorresponding residual values when estimating subsequent layers.

In various embodiments of the invention, a plurality of candidate symbolvalues, cp[i], may be selected for a layer i. The number of candidatesymbol values cp[i] is less than the total number of constellationpoints associated with the layer. The first of the candidate symbolvalues may be selected, for example, corresponding to a candidate symbolvalue associated with the first layer. The value of the first candidatesymbol may be cancelled from cancelled from the vector Y. A residualvalue may be stored. A subsequent candidate symbol value may be selectedcorresponding to the j_(th) candidate symbol associated with the firstlayer. The value of the subsequent candidate symbol may be cancelledfrom the vector Y. A subsequent residual value may be stored.

After computing a residual value for each of the plurality of cp[i]candidate symbols associated with the first layer, each of the pluralityof cp[i] residual values may be utilized to repeat the procedure for the(i+1)^(th) layer. The total number of candidate vectors may berepresented according to the following equation: $\begin{matrix}{L^{\prime} = {\prod\limits_{i = 1}^{NSS}{{cp}\lbrack i\rbrack}}} & {{equation}\quad\lbrack 18\rbrack}\end{matrix}$where M[i]>cp[i] for at least a portion of the values i within the rangeof values 1, 2, . . . , NSS. Correspondingly, L>L′. For each of thecandidate vectors L′ soft information may be computed as described inequation[15].

FIG. 3 is a graph illustrating exemplary results from simulation of asystem for minimum mean squared error with soft interferencecancellation (MMSE-SIC) based on suboptimal maximum likelihood (ML)detection for a MIMO-OFDM wireless system, in accordance with anembodiment of the invention. Referring to FIG. 3 there is shown a graphbased on a full-ML 302, a graph based on a SIC-ML[30, 10] 304, a graphbased on a SIC-ML[24, 10] 306, a graph based on a SIC-ML[16, 8] 308, anda graph based on MMSE-LE 310.

The graphs 302, 304, 306, 308, and 310 in FIG. 3 may representsimulation results illustrating packet error rates (PER) at varioussignal to noise ratio (SNR) levels in an 802.11n wireless local areanetwork (WLAN) MIMO communications system comprising a transmitter 104(FIG. 1) that utilizes 2 transmitting antennas, and a receiver 124 thatutilizes 2 receiving antennas. The system may comprise 2 spatialstreams, utilizing the 64 quadrature amplitude modulation (QAM)modulation type for each spatial stream, and utilizes a coding rate of⅚. The graph 302 may represent results from a full-ML system. A full-MLdetector may comprise a ML detector that generates a number of errorvectors equal to a number of constellation points in a constellation mapfor a received signal comprising a plurality of spatial streams. In thecontext of FIG. 3, this may comprise generation of approximately 4,096error vectors. The graph 304 may represent an embodiment of theinvention in which 30 error vectors may be generated in a first spatialstream, with 10 error vectors generated in a second spatial stream foreach error vector generated in the first spatial stream. The conditionsspecified for graph 304 may result in the generation of approximately300 error vectors. The PER versus SNR performance of an exemplarySIC-ML[30, 10] system may be approximately equivalent to that of anexemplary full-ML system.

The graph 306 may represent an embodiment of the invention in which 24error vectors may be generated in a first spatial stream, with 10 errorvectors generated in a second spatial stream for each error vectorgenerated in the first spatial stream. The conditions specified forgraph 306 may result in the generation of approximately 240 errorvectors. The PER versus SNR performance of an exemplary SIC-ML[24, 10]system may be such that an exemplary SIC-ML[30, 10] system may maintaina PER of 0.01, or the equivalent of a 1% PER, at a SNR of approximately0.25 dB lower than that of the exemplary SIC-ML[24, 10] system.

The graph 308 may represent an embodiment of the invention in which 16error vectors may be generated in a first spatial stream, with 8 errorvectors generated in a second spatial stream for each error vectorgenerated in the first spatial stream. The conditions specified forgraph 308 may result in the generation of approximately 128 errorvectors. The PER versus SNR performance of an exemplary SIC-ML[16, 8]system may be such that an exemplary SIC-ML[30, 10] system may maintaina PER of 0.01, or the equivalent of a 1% PER, at a SNR of approximately2 dB lower than that of the exemplary SIC-ML[16, 8] system.

The graph 310 may represent results from a MMSE-LE system that may notutilize ML detection. The PER versus SNR performance of an exemplaryMMSE-LE system may be such that an exemplary SIC-ML[30, 10] system maymaintain a PER of 0.01, or the equivalent of a 1% PER, at a SNR ofapproximately 4 dB lower than that of the exemplary MMSE-LE system.

FIG. 4A is a diagram illustrating an exemplary method for softinformation calculation, in accordance with an embodiment of theinvention. Referring to FIG. 4A, there is shown a plurality of N1 pointsassociated with layer 1 comprising 402 a, 402 b, 402 c and 402 d, aplurality of N1*N2 points associated with layer 2 comprising 412 a, 412b, 412 c, 414 a, 414 b, 414 c, and a plurality of N1*N2*N3 pointsassociated with layer 3 comprising 422 a, 422 b, 424 a, 424 b, 426 a,426 b, 432 a, 432 b, 434 a, 434 b, 436 a, and 436 b. The number ofpoints N1*N2*N3 may comprise a plurality of L candidate vectors that maybe utilized to calculate soft information. The set of candidate vectorsmay be denoted by the variable C. Each of the points may represent avalue for a symbol. For each point in layer 1, there may be plurality ofN2 points in layer 2, for example associated with point 402 a in layer 1may be points 412 a, 412 b, and 412 c in layer 2. For each point inlayer 2, there may be N3 points in layer 3, for example associated withpoint 412 a in layer 2 may be points 422 a, and 422 b in layer 3. Agroup of points spanning the layers 1, 2 and 3 may comprise a candidatevector, for example the points 402 a, 412 a, and 422 a. In variousembodiments of the invention, a metric, M=∥Y−Hs∥ may be computed foreach candidate vector contained in the set C. The L candidate vectorsmay be sorted in increasing order, for example, based on the metric M.

FIG. 4B is a diagram illustrating an exemplary soft informationcalculation, in accordance with an embodiment of the invention.Referring to FIG. 4B there is shown a plurality of symbols 440, 442,444, 446, 448, 450, 452, 454, 456, 458, 460, 462, 464, 466, and 468. Inthe example illustrated in FIG. 4B, there are 3 layers. 64-pointquadrature amplitude modulation (64 QAM) is utilized for each layer.Consequently, each symbol comprises 6 bits, and each candidate vectorcomprises 18 bits. Thus, soft information may be computed for each ofthe 18 bits for each of the L candidate vectors. A metric, M_q may becomputed for each of the L candidate vectors as indicated by q, where qmay be set to a value among the range 1, 2, . . . , L. For example, ametric M_1 may be computed for the first candidate vector.

Symbols 440, 442, and 444 may represent symbols associated withcandidate vector 1. Symbols 446, 448, and 450 may represent symbolsassociated with candidate vector 2. Symbols 452, 454, and 456 mayrepresent symbols associated with candidate vector 3. Symbols 458, 460,and 462 may represent symbols associated with candidate vector 4.Symbols 464, 466, and 468 may represent symbols associated withcandidate vector L.

Symbols 440, 446, 452, 458 and 464 may represent symbols, s1, associatedwith layer 1. Symbols 442, 448, 454, 460 and 466 may represent symbols,s2, associated with layer 2. Symbols 444, 450, 456, 462 and 468 mayrepresent symbols, s3, associated with layer 3.

Equation[15] may be utilized to compute an LLR for the most significantbit (MSB) associated with layer 1. In candidate vector 1, the binaryvalue of the MSB in the symbol 440 may be 0. The corresponding metricmay be M_1. In candidate vector 4, the binary value of the MSB in thesymbol 458 may be 1. The corresponding metric may be M_4. The softinformation associated with the MSB of symbol s1, may be computed fromthe LLR where LLR=(M_1−M_4).

Since, in various embodiments of the invention, the L candidates mayrepresent a subset of the full combination of candidate vectors, it ispossible for an m^(th) MSB associated with a p^(th) symbol to have abinary value of 0, or a binary value of 1, in each of the L candidatevectors. As highlighted in the example illustrated in FIG. 4B, thesecond MSB associated with the second symbol, 442, 448, 454, 460 and466, is equal to a binary value of 0 in each of the L candidate vectors.In such cases, the LLR may be approximated LLR=(M_1+K), where K mayrepresent a positive value.

FIG. 5 is a flow chart illustrating exemplary steps for softinterference cancellation (SIC), in accordance with an embodiment of theinvention. Referring to FIG. 5, in step 502, a channel estimation, Ĥ,may be computed. In step 504, variables may be initialized. The layerindex i may indicate a layer, and may be initialized i=1 to indicate afirst layer. The variable Y may refer to a received signal. The signalvariable Y₁ may refer to a layer 1 signal, and may be initialized Y₁=Y.The initialization value of the signal variable Y₁ may indicate that, atinitialization, no layers have been cancelled from the received signal.Consequently, the layer 1 signal variable Y₁ may be equal to thereceived signal Y. The received signal Y may be represented by a columnvector Y. A layer channel estimation, Ĥ, may refer to a channel estimateassociated with current layer, and may be initialized Ĥ_(i)=Ĥ. The layerindex, i, may refer to a layer as detected based on signals received bya plurality of receiving antennas 132,134, . . . ,136, for example. Instep 506, an MMSE estimation, Ŝ_(i), may be generated based on thecolumn vector Y and on the layer channel estimation Ĥ_(i). The MMSEestimation may be represented, Ŝ_(i)=W_(i)Y_(i), where the variable imay represent the current value for the layer index, and the variableW_(i) may be computed from the MMSE equalization equation[5] for thelayer i. The MMSE estimation Ŝ_(i) may be represented by a columnvector. The column vector Ŝ₁ may comprise a plurality of layers L(1),L(2), . . . , L(NSS) corresponding to a plurality of layers associatedwith transmitting antennas 112,114, . . . ,116, for example.

In step 508, the spatial streams associated with the column vector Ŝ_(i)may be sorted according to a corresponding signal to noise ratio (SNR)value. For example, the spatial streams may be sorted in order ofdecreasing SNR value. In an exemplary sorted order, the layers may besorted, L(i), . . . , L(NSS), where L(i) may be associated with thelargest SNR value among the sorted layers, and L(NSS) may be associatedwith the smallest SNR value. The variable i may represent the currentvalue for the layer index, for example during the first iteration in theflow chart, i=1. In step 510, the layer associated with the largest SNRvalue estimate may be selected. The selected layer may be L(i), forexample. In step 512, a plurality of constellation points, associatedwith the layer L(i) may be selected. The plurality of constellationpoints may be in the vicinity of the corresponding MMSE estimation forthe layer L(i), Ŝ_(L(i)). The plurality of constellation points may berepresented MMSE-CONS[L(i),1], MMSE-CONS[L(i),2], . . . ,MMSE-CONS[L(i),cp(i)], where cp(i) may represent a number ofconstellation points selected. The constellation point index associatedwith layer L(i), k(i), may be initialized to a value k(i)=1.

Step 514 may determine if the layer index has reached the last of theplurality of NSS layers contained in the received signal Y. If step 514has determined that the layer index has not reached the last of theplurality of NSS layers, in step 516, a subsequent signal variableY_(i+1) may be computed by cancelling interference from the signalvariable Y_(i). The interference, which may be cancelled, may becomputed based on a selected constellation point, MMSE-CONS[L(i),k(i)],from among the plurality of constellation points associated with thelayer L(i), and computed based on an L(i)^(th) column from the channelestimation matrix Ĥ, ĥ_(L(i)). In step 518, and a subsequent layerchannel estimate matrix Ĥ_(i+1) may be computed by removing the columncorresponding to column vector ĥ_(L(i)) from the current layer channelestimate matrix Ĥ_(i). The column vector, ĥ_(L(i)), may represent achannel estimate associated with a layer L(i) that was transmitted by acorresponding transmitting antenna 112. In step 518, the layer index, i,may also be incremented. Step 506 may follow step 518. In step 506, thatfollows step 518, a subsequent MMSE estimation may be computed based onthe subsequent layer channel estimate matrix, and on the subsequentsignal variable.

If step 514 determines that the layer index has reached the last of theplurality of NSS layers, step 520, may determine if the current valuefor the constellation point index for the layer L(i), k(i), is greaterthan the value that indicates the total number of constellation pointsselected for the layer L(i), cp(i). If step 520 determines that theconstellation point index is not greater than the total number ofconstellation points selected for the layer, in step 522, theconstellation point MMSE-CONS[L(i),k(i)] may be added to a candidatelist. The candidate list may comprise a set candidate vectors for whichsoft information may be computed. In step 522, the constellation pointindex may also be incremented. Step 520 may follow step 522.

If step 520 determines that the constellation point index is greaterthan the total number of constellation points selected for the layer, instep 524, the layer index, i, may be decremented, and the constellationpoint index associated with the current layer, k(i), may be incremented.Step 526 may determine if the current value for the layer index is equalto 0. If step 526 determines that the current value for the layer indexis equal to 0, the process for constructing a candidate list may becompleted. The process may therefore end.

If step 526 determines that the current value for the layer index is notequal to 0, step 528 may determine if the current value for theconstellation point index for the current layer, k(i), is greater thanthe value that indicates the total number of constellation pointsselected for the layer cp(i). If step 528 determines that the value ofk(i) is greater than the value of cp(i), step 524 may follow step 528.If step 528 determines that the value of k(i) is not greater than thevalue of cp(i), step 516 may follow step 528. In step 516, which followsstep 528, the process of cancellation of interference and generation ofMMSE estimates may continue.

FIG. 6 is a flow chart illustrating exemplary steps in the selection ofconstellation points to be utilized in MMSE-SIC for a spatial streamcomprising a symbol received by an antenna in accordance with anembodiment of the invention. Referring to FIG. 6, in step 602 aconstellation point counter j may be set equal to 1. In step 604, anumber of constellation points in a constellation map for a symbolreceived in an i^(th) spatial stream by an n^(th) receiving antenna maybe determined. The number of selected constellation points may beindicated by the variable cp[i], and the total number of constellationpoints may be indicated by the variable NCONS[i]. In step 606 an errormagnitude may be computed where q_(j) may represent the j^(th)constellation point in the constellation, and ŝ_(i) may represent anestimated value for a transmitted symbol s_(i) associated with thei^(th) layer.

Step 608 may determine if the current constellation point is the lastconstellation point in the constellation map. If not, step 610 mayincrement the constellation point counter to reference a subsequentconstellation point. Step 606 may follow. If step 608 determines thatthe current constellation point is the last constellation point in theconstellation map, step 612 may select cp[i] number of constellationpoints with the smallest magnitude errors computed in step 606. Theselected constellation points may be stored as constellation vectorMMSE-CONS[i, 1,2, . . . cp[i]].

Various aspects of a method for minimum mean square error softinterference cancellation (MMSE-SIC) based sub-optimal maximumlikelihood (ML) detection for a multiple input multiple output (MIMO)wireless system may comprise selecting at least one constellation pointin a constellation map based on at least one of a plurality of receivedsymbols. A number of the at least one constellation point may be lessthan or equal to a number of previously selected constellation points ina previous constellation map. At least one of the plurality of receivedsymbols may be decoded based on the selected at least one constellationpoint.

Aspects of a system for MMSE-SIC based sub-optimal ML detection for aMIMO wireless system may comprise a detector that selects at least oneconstellation point in a constellation map based on at least one of aplurality of received symbols. A number of the at least oneconstellation point may be less than or equal to a number of previouslyselected constellation points in a previous constellation map. At leastone of the plurality of received symbols may be decoded based on theselected at least one constellation point.

Another aspect of a method for MMSE-SIC based sub-optimal ML detectionfor a MIMO wireless system may comprise sorting a received RF signalinto a plurality of layers based on an SNR, generating at least oneestimated symbol based on one of the plurality of layers, and generatingat least one cancellation signal resulting from canceling interferencefrom the received RF signal based on each of the at least one estimatedsymbol. The aspect may further comprise generating at least onesubsequent estimated symbol based on a subsequent one of the pluralityof layers, and generating at least one subsequent cancellation signalresulting from canceling subsequent interference from each of the atleast one cancellation signal based on each of the at least onesubsequent estimated symbol. Soft information may be derived based on atleast one estimated symbol, and the at least one subsequent estimatedsymbol. The received RF signal may be decoded based on the derived softinformation.

Accordingly, the present invention may be realized in hardware,software, or a combination of hardware and software. The presentinvention may be realized in a centralized fashion in at least onecomputer system, or in a distributed fashion where different elementsare spread across several interconnected computer systems. Any kind ofcomputer system or other apparatus adapted for carrying out the methodsdescribed herein is suited. A typical combination of hardware andsoftware may be a general-purpose computer system with a computerprogram that, when being loaded and executed, controls the computersystem such that it carries out the methods described herein.

The present invention may also be embedded in a computer programproduct, which comprises all the features enabling the implementation ofthe methods described herein, and which when loaded in a computer systemis able to carry out these methods. Computer program in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: a) conversion to anotherlanguage, code or notation; b) reproduction in a different materialform.

While the present invention has been described with reference to certainembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted withoutdeparting from the scope of the present invention. In addition, manymodifications may be made to adapt a particular situation or material tothe teachings of the present invention without departing from its scope.Therefore, it is intended that the present invention not be limited tothe particular embodiment disclosed, but that the present invention willinclude all embodiments falling within the scope of the appended claims.

1. A method for processing received information wireless communicationsystem, the method comprising: selecting at least one constellationpoint in a constellation map for received signals in a multiple inputmultiple output (MIMO) system based on at least one of a plurality ofreceived symbols, wherein a number of said at least one constellationpoint is less than or equal to a number of previously selectedconstellation points in a previous constellation map; and decoding saidat least one of a plurality of received symbols based on said selectedat least one constellation point.
 2. The method according to claim 1,further comprising computing at least one magnitude comprising anumerical difference between said at least one constellation point andsaid one of said plurality of received symbols.
 3. The method accordingto claim 1, further comprising computing at least one subsequentmagnitude comprising a numerical difference between at least onesubsequent constellation point and said at least one of a plurality ofreceived symbols.
 4. The method according to claim 3, further comprisingderiving soft information, based on: a selected minimum one from atleast one magnitude; and a selected minimum one from said at least onesubsequent magnitude.
 5. The method according to claim 4, wherein saiddecoding said at least one of a plurality of received symbols furthercomprises deriving at least one binary bit based on said softinformation.
 6. The method according to claim 1, wherein said decodingdetermines a binary value for at least one binary bit from said at leastone of a plurality of received symbols.
 7. The method according to claim1, wherein said number of at least one constellation point, and at leastone said number of previously selected constellation points in aprevious constellation map is based on at least one of: a signal tonoise ratio (SNR), and a packet error rate (PER).
 8. The methodaccording to claim 1, further comprising estimating said at least one ofa plurality of received symbols based on at least one of a selected saidat least one constellation point.
 9. A system for processing receivedinformation in a wireless communication system, the system comprising: adetector that selects at least one constellation point in aconstellation map for received signals in a multiple input multipleoutput (MIMO) system based on at least one of a plurality of receivedsymbols, wherein a number of said at least one constellation point isless than or equal to a number of previously selected constellationpoints in a previous constellation map; and a decoder that decodes saidat least one of a plurality of received symbols based on said selectedat least one constellation point.
 10. The system according to claim 9,wherein said detector computes at least one magnitude comprising anumerical difference between said at least one constellation point andsaid one of said plurality of received symbols.
 11. The system accordingto claim 9, wherein said detector computes at least one subsequentmagnitude comprising a numerical difference between at least onesubsequent constellation point and said at least one of a plurality ofreceived symbols.
 12. The system according to claim 11, wherein saiddetector derives soft information, based on: a selected minimum one fromat least one magnitude; and a selected minimum one from said at leastone subsequent magnitude.
 13. The system according to claim 12, whereinsaid decoding said at least one of a plurality of received symbolsfurther comprises deriving at least one binary bit based on said softinformation.
 14. The system according to claim 9, wherein said decodingdetermines a binary value for at least one binary bit from said at leastone of a plurality of received symbols.
 15. The system according toclaim 9, wherein said number of at least one constellation point, and atleast one said number of previously selected constellation points in aprevious constellation map is based on at least one of: a signal tonoise ratio (SNR), and a packet error rate (PER).
 16. The systemaccording to claim 9, wherein said detector estimates said at least oneof a plurality of received symbols based on at least one of a selectedsaid at least one constellation point.
 17. A method for processingreceived information in a wireless communication system, the methodcomprising: sorting a received RF signal in a multiple input multipleoutput (MIMO) system into a plurality of layers based on an SNR;generating at least one estimated symbol based on one of said pluralityof layers; generating at least one cancellation signal resulting fromcanceling interference from said received RF signal based on each ofsaid at least one estimated symbol; generating at least one subsequentestimated symbol based on a subsequent one of said plurality of layers;generating at least one subsequent cancellation signal resulting fromcanceling subsequent interference from each of said at least onecancellation signal based on each of said at least one subsequentestimated symbol; deriving soft information based on said at least oneestimated symbol, and said at least one subsequent estimated symbol; anddecoding said received RF signal based on said derived soft information.18. The method according to claim 17, further comprising repeating saidgenerating said at least one subsequent estimated symbol, and saidgenerating at least one subsequent cancellation signal until saidsubsequent one of said plurality of layers is a last of said pluralityof layers.
 19. The method according to claim 18, further comprisinggenerating elements in a constellation vector based on a number of saidat least one cancellation signals, and a number of said at least onesubsequent cancellation signals for each said subsequent one of saidplurality of layers.
 20. The method according to claim 19, furthercomprising computing at least one magnitude comprising a numericaldifference between said received RF signal and at least one said elementin said constellation vector.
 21. The method according to claim 20,further comprising computing at least one subsequent magnitudecomprising a numerical difference between said received RF signal and asubsequent at least one said element in said constellation vector. 22.The method according to claim 21, further comprising deriving said softinformation based on a difference between a minimum said at least onemagnitude, and a minimum said at least one subsequent magnitude.
 23. Themethod according to claim 17, wherein said estimated symbol is a minimummean square error with linear equalization (MMSE-LE) estimate.
 24. Themethod according to claim 17, wherein said estimated symbol is aconstellation point in a constellation map.
 25. The method according toclaim 17, wherein said subsequent estimated symbol is based on saidcancelled interference.